Pearson, IBM Watson and cognitive enhancement technologies in education

Ben Williamson

Image: Atomic Taco

The world’s largest edu-business, Pearson, partnered with one of the world’s largest computing companies, IBM, at the end of October 2016 to develop new approaches to education in the ‘cognitive era.’ Their partnership was anticipated earlier in the year when both organizations produced reports about the future trajectories of cognitive computing and artificial intelligence for personalizing learning. I wrote a piece highlighting the key claims of both at the time, and have previously published some articles tracing both Pearson’s interests in big data and IBM’s development of cognitive systems for learning. The announcement of their partnership is the next step in their efforts to install new machine intelligences and cognitive systems into educational institutions and processes.

At first sight, it might seem surprising that IBM and Pearson have partnered together. Their reports would suggest they were competing to produce a new educational market for artificially intelligent or cognitive systems applications. Pearson, however, has had a bad couple of years, with falling revenue and reputational decline, which appears to have resulted in the closure of its own in-house Center for Digital Data, Analytics and Adaptive Learning. IBM, meanwhile, has been marketing its cognitive computing systems furiously for use in business, government, healthcare, education, and other sectors. The key to the partnership is that, despite its business troubles, Pearson retains massive penetration into schools and colleges through its digital courseware, while IBM has spent years developing and refining its cognitive systems. A mutually beneficial strategic business plan underpins their partnership.

The Pearson-IBM partnership also taps into current enthusiasm and interest in new forms of machine-based intelligence. This is reflected, for example, in the recent establishment  of the Leverhulme Centre for Future Intelligences at the University of Cambridge, a White House report on preparing the future of artificial intelligence, and a Partnership on AI established by Facebook, Amazon, Google, IBM and Microsoft. The central tenet of the partnership on AI is that ‘artificial intelligence technologies hold great promise for raising the quality of people’s lives and can be leveraged to help humanity address important global challenges such as climate change, food, inequality, health, and education.’

Together, these developments point to a growing contemporary concern with forms of machine intelligence that are sometimes described as ‘weak’ or ‘narrow’ forms of AI. Weak or narrow AI includes techniques such as cognitive computing, deep learning, genetic algorithms, machine learning, and other automated, algorithmic processes, rather than aspiring to ‘strong’ or ‘general’ models of AI which assume computers might become autonomous superintelligences. A recent report on future computing produced by the Human Brain Project noted that:

The power of these innovations has been increased by the development of data mining and machine learning techniques, that give computers the capacity to learn from their ‘experience’ without being specifically programmed, constructing algorithms, making predictions, and then improving those predictions by learning from their results, either in supervised or unsupervised regimes. In these and other ways, developments in ICT and robotics are reshaping human interactions, in economic activities, in consumption and in our most intimate relations.

Ultimately, such technologies can be described as cognitive or intelligent because they have been built to learn and adapt in ways that are inspired by the human brain. Neuroscientific insights into the plasticity of the brain, how it adapts to input and stimuli from the social environment, have been at the centre of the current resurgence of interest in machine intelligence.

So what is education likely to look like if the glossy imaginary projected by Pearson and IBM of learning in the cognitive era materializes in the future?

Learning machines

The key technology underpinning their ambitions is Watson, IBM’s highly-publicized cognitive supercomputing system. The IBM webpages describe Watson as ‘a cognitive technology that can think like a human,’ and which has the capacity to:

• Understand: With Watson, you can analyze and interpret all of your data, including unstructured text, images, audio and video.
• Reason: With Watson, you can provide personalized recommendations by understanding a user’s personality, tone, and emotion.
• Learn: With Watson, you can utilize machine learning to grow the subject matter expertise in your apps and systems.
• Interact: With Watson, you can create chat bots that can engage in dialog.

Key to the way IBM is marketing Watson is that it has been built with extraordinary flexibility, with Watson APIs and starter code provided to allow organizations to build their own apps and products.

Though IBM has been promoting cognitive computing in education for a few years—in 2013 it produced a glossy visualization of the classroom in 5 years time, a ‘classroom that will learn you’—it is now firmly seeking to establish Watson in the educational landscape. IBM Watson Education, it claims, ‘is bringing education into the cognitive era’:

We are transforming the learning experience through personalization. Cognitive solutions that understand, reason and learn help educators gain insights into learning styles, preferences, and aptitude of every student. The results are holistic learning paths, for every learner, through their lifelong learning journey.

One of the key applications IBM has developed is a data-based performance tracking tool for schools and colleges called IBM Watson Element for Educators:

Watson Element is designed to transform the classroom by providing critical insights about each student – demographics, strengths, challenges, optimal learning styles, and more – which the educator can use to create targeted instructional plans, in real-time. Gone are the days of paper-based performance tracking, which means educators have more face time with students, and immediate feedback to guide instructional decisions.

Designed for use on an iPad so it can be employed directly in the classroom, Element can capture conventional performance information, but also student interests and other contextual information, which it can feed into detailed student profiles. This is student data mining that goes beyond test performance to social context (demographics) and psychological classification (learning styles). It can also be used to track whole classes, and automatically generates alerts and notifications if any students are off-track and need further intervention.

Another, complementary application is IBM Watson Enlight for Educators. Enlight is designed as a tool to support teachers to personalize their instructional techniques and content:

IBM Watson Enlight embodies three guiding principles: 1. Know Me: empower teachers with a comprehensive view of relevant data to help understand each student’s strengths and areas of growth 2. Guide Me: provide teachers with guidance as to how best to support each student 3. Help Me: support teachers with curated, personalized learning content and activities aligned with each student’s needs.

The application is marketed as a support system for understanding a class and the individual students in it, and for generating ‘actionable insights’ to ‘target learning experiences. ‘Teachers can optimize their time and impact throughout the year using actionable, on-demand insights about their students,’ it claims, and then ‘craft targeted learning experiences on-the-fly from content they trust.’ In many ways, these applications are extraordinarily similar to those being promoted for schools by companies like Facebook, with its Summit Personalized Learning platform, or AltSchool’s Playlist and Progression tools.

The partnership with Pearson will allow Watson to penetrate into educational institutions at a much bigger scale than it could do on its own, thanks to the massive reach of Pearson’s courseware products. Specifically, the partnership is focusing on the higher education sector (though time will tell whether it further migrates into the schools sector). The press release issued by Pearson stated that its new global education partnership would ‘make Watson’s cognitive capabilities available to millions of college students and professors’:

Pearson and IBM are innovating with Watson APIs, education-specific diagnostics and remediation capabilities. Watson will be able to search through an expanded set of education resources to retrieve relevant information to answer student questions, show how the new knowledge they gain relates to their own existing knowledge and, finally, ask them questions to check their understanding.

Strikingly, it proposes that Watson will act as a:

flexible virtual tutor that college students can access when they need it. With the combination of Watson and Pearson, students will be able to get the specific help they need in real time, ask questions and be able to recognize areas in which they still need help from an instructor.

The press release issued by IBM added that Watson would be ‘embedded in the Pearson courseware’:

Watson has already read the Pearson courseware content and is ready to spot patterns and generate insights.  Serving as a digital resource, Watson will assess the student’s responses to guide them with hints, feedback, explanations and help identify common misconceptions, working with the student at their pace to help them master the topic.

What Watson will do, then, is commit the entirety of Pearson’s content to its computer memory, and then, by constantly monitoring each individual student, cognitively calculate the precise content or structure of a learning experience that would best suit or support that individual.

The partnership is ultimately the material operationalization of a shared imaginary of machine intelligences in education that both IBM and Pearson have been projecting for some time. But this imaginary is slowly moving out of the institutional enclosures of these organizations to become more widely perceived as desirable and attainable in the future, and it is beginning to animate policy ideas as well as technical projects. The White House report on AI, for example, specifically advocates the development of AI digital tutors for use in education, and has suggested the need for a new technical agency within the US Department for Education that is modelled on its defence research agency DARPA. The think tank the Center for Data Innovation has also produced a report on ‘the promise of AI‘ that admiringly promotes Watson applications such as its automated Teacher Advisor.

Cognitive enhancement technologies

Underpinning these efforts is a shared vision of how machine intelligence might act as cognitive-enhancement technologies in educational settings, though we clearly need to be cautious about the extent to which the technology will live up to its futuristic hype. As educational technology critic Audrey Watters has recently argued, ‘the best way to predict the future is to issue a press release.’ IBM and Pearson are both busily marketing their vision of the cognitive future of education because their businesses depend on it. For them, it’s necessary to suggest that people today are at a cognitive deficit when faced with the complexities of the technologized era, so they can sell products offering cognitive enhancement.

The promise of cognitive computing for IBM, as stated in its recent white paper on ‘Computing, cognition and the future of knowing,’ is not just of more ‘natural systems’ with ‘human qualities,’ but a fundamental reimagining of the ‘next generation of human cognition, in which we think and reason in new and powerful ways’:

It’s true that cognitive systems are machines that are inspired by the human brain. But it’s also true that these machines will inspire the human brain, increase our capacity for reason and rewire the ways in which we learn.

These are extraordinary claims that put companies like IBM and Pearson in the cognitive-enhancement business. They have positioned themselves at the vanguard of the generation of hybrid ‘more-than-human’ cognition, learning and thinking.

Clearly there may be consequences of the development of cognitive enhancement technologies and machine intelligences in education. These technologies could ultimately become responsible for establishing the educational pathway and progress of millions of students. They could ‘learn’ some bad habits, like Microsoft’s infamous AI chatbot. They could be found to discriminate against certain groups of students, and reinforce and reproduce existing social inequalities. Privacy and data protection is an obvious issue as supposedly clever technologies ingest all the intimate details of individual students and store them in vast databanks on the IBM cloud. If Watson scales across Pearson’s content and courseware, it is ultimately going to be able to collect and data-mine huge amounts of information about potentially millions of students worldwide.

Moreover, access to these technologies won’t be cheap for institutions. This could lead to competitive cognitive advantage for students from wealthy institutions, whose learning and development may be supported by cognitive enhancement technologies. A new form of hybrid cognitive capital may become available for students at institutions that invest in these cognitive systems. Given that Pearson’s own global databank of country performance, the Learning Curve, compares education systems according to students’ ‘cognitive skills,’ measuring national cognitive capital as a comparative advantage in the ‘global race’ could also become attractive to government agencies.

Regarding this last point, IBM and Pearson also anticipate the development of real-time adaptive forms of governance in education. Both Pearson and IBM are trying to bypass the cumbersome bureaucratic systems of testing and assessment by creating real-time analytics that perform constant diagnostics and adaptive, personalized intervention on the individual. Pearson’s previous report on AI in education spells this out clearly:

Once we put the tools of AIEd in place as described above, we will have new and powerful ways to measure system level achievement. … AIEd will be able to provide analysis about teaching and learning at every level, whether that is a particular subject, class, college, district, or country. This will mean that evidence about country performance will be available from AIEd analysis, calling into question the need for international testing.

Although the current partnership with IBM is focused on college students, then, this is just part of a serious aspiration to govern the entire infrastructure of education systems through real-time analytics and machine intelligences, rather than through the infrastructure of test-based accountability that currently dominates schools and colleges.

Educational institutions are by now well used to accountability systems that involve collecting and processing test scores to produce performance measures, comparisons and ratings. IBM and Pearson are proposing to make cognitive systems orchestrate this infrastructure of accountability. As Adrian Mackenzie has put it, ‘cognitive infrastructures’ such as Watson ‘present problems of seeing, hearing, checking and comparing as no longer the province of human operators, experts, professionals or workers … but as challenges set for an often almost Cyclopean cognition to reorganise and optimise.’ IBM and Pearson are seeking to sink a cognitive infrastructure of accountability into the background of education, one which is intended to not just to measure and compare performance, but to reorganize and optimize whole systems, institutions and individuals alike.